Local quantile regression
By Vladimir Spokoiny, Weining Wang, Wolfgang Karl Härdle. 2010
65 citations recorded by the source corpus.
Conditional quantile curves provide a comprehensive picture of a response contingent on explanatory variables. Quantile regression is a technique to estimate such curves. In a flexible modeling framework, a specific form of the quantile is not a priori fixed. Indeed, the majority of applications do not per se require specific functional forms. This motivates a local parametric rather than a global fixed model fitting approach. A nonparametric smoothing estimate of the conditional quantile curve requires to consider a balance between local curvature and variance. In this paper, we analyze a method based on a local model selection technique that provides an adaptive estimate. Theoretical properties on mimicking the oracle choice are offered and applications to stock market and weather analysis are presented.
Subfields: Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/835d712071de8b9b2ef2d8e5c6bc648c7f150e05
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
In Statistics and Probability
- Vladimir Spokoiny
- D. Chetverikov
- Maxim Panov
- Yingcun Xia
- Arnak S. Dalalyan
- Ohad Shamir
- Jiti Gao
- B. Recht
Citations in the corpus
- Parametric estimation. Finite sample theory (2011, methodology): “The proposed methodology has been successfully applied to the problem of bandwidth selection in local quantile regression, Spokoiny et al. (2012).”